The present invention relates generally to cardiac rhythm management systems, and more particularly, it pertains to a system and method of classification and detection of cardiac signals.
Analysis of cardiac signals, which is routinely performed in electrocardiography is generally based on visual inspection to quantify or qualify wave morphology for the purpose of identifying and classifying abnormal patterns. Certain morphological characteristics of commonly recorded signals have high diagnostic value. The shape and inter arrival times of R-waves recorded in the electrocardiogram generally provide a wealth of information about the state of the heart. Accordingly, automated approaches for identifying and classifying abnormalities in signals such as cardiac signals have sought to use a signal""s significant morphologic characteristics.
However, given the wide diversity of possible shapes for cardiac signals, it is usually not possible for an automatic approach to identify significant characteristics that can be used for unambiguous classification. Rather, automated classification approaches generally compare the entire morphological shape of a signal with the shape of similar signals with known abnormalities but without particular regard to the specific characteristics that the signals contain. Alternatively, automated classification approaches restrict the automated examination only to those signals which are essentially normal and use detailed metrics (for example QRS width, QT interval or ST segment amplitude) of the essentially normal morphology for classifying abnormalities.
Despite its importance in the analysis of biologic signals, the automated and accurate identification and quantification of the significant morphological characteristics (for example turns, peaks, knees, inflection points, and the like) in any cardiac signal (both abnormal as well as normal) is still in a developing stage. Existing methods have used the concept of sharpness (for example to detect R-waves) but have had limited success. This is due in part to the overly simplistic mathematical treatment this concept has received, as reflected in the rudimentary algorithms used for these measurements. Most of the current detection methods rely on three point interpolations to measure sharpness. The simplest and most commonly used methods for measuring peaks of R-waves are based upon Taylor-series approximations to estimate the second derivative of the sensed signal. This formula utilizes highly local information (the point at the peak and its two close neighbors) ignoring nearby points which may contribute to signal peak. Other popular approaches utilize less local data, such as the peak and two adjacent extrema. All of these methods, which rely on three-point estimates of sharpness, may produce inaccurate estimates, if waveforms are complex or are contaminated with noise. Thus, a need exists for automated identification and classification of peaks, knees, inflection points, and the like in sensed cardiac signals that takes into account wave scale and complexity that can yield a more accurate estimate of peaks for identifying and classifying abnormalities in cardiac signals.
The present subject matter provides a curvature based method of selecting features from electrophysiologic signals for purpose of complex identification and classification. According to one aspect of the present subject matter, this is accomplished by sensing a cardiac signal (sensing the cardiac signal includes sensing complexes continuously on a real-time basis) and computing curvatures on a sample point-by-sample point basis (X1, X2, X3, . . . XI) on the sensed cardiac signal on a continuous basis. In one embodiment, the curvature at the sample points X1, X2, X3, . . . XI are computed by fitting a cubic least square error curve (using N number of sample points) to the sensed cardiac signal. In this embodiment, N is an odd number, and the sample point (where the curvature is computed) is at a mid point of the N number of sample points.
Also on a continuous basis, features of significant interest are extracted from the computed curvatures. In some embodiments, this is accomplished by comparing the 30 computed curvatures at the sample points X1, X2, X3, . . . XI to a set of predetermined threshold values. In some embodiments, the features are extracted based on computing features such as a time when the feature occurs, an amplitude of the feature, and other similar features at each of the sample points and comparing them to a set of threshold values.
Also on a continuous basis a set of features associated with a first complex in the sensed cardiac signal are identified and separated from the continuously computed and extracted features upon detecting a second subsequent complex. The second subsequent complex is a complex that is adjacent to the first complex and occurs substantially immediately after the first complex. This process of identifying and separating extracted features repeats itself from one sensed complex to another subsequent sensed complex on a real time basis. One reason for identifying and separating the set of features associated with the first complex is to prevent the features associated with the first complex from mixing with the features associated the second subsequent complex. Separating the features associated with first complex aids in classifying the sensed first complex.
Next, the process includes identifying a fiducial feature from the separated set of extracted features associated with the first complex and aligning the separated set of features with respect to the identified fiducial feature. In one embodiment, fiducial feature is identified based on comparing the times when each of the separated set of features occur with a time when a complex associated with the separated set of features is detected on the sensed cardiac signal, and selecting a feature from the separated set of features that is closest in time to the time when the complex associated with the separated set of features was detected. One reason for using a time when a complex is detected (such as R wave) in identifying the fiducial feature, is because the detection of a complex in a sensed cardiac signal is generally more reliable and consistent.
Next, the process includes aligning the separated set of features around the identified fiducial feature. Aligning the separated set of features around the identified fiducial feature aids in normalizing each of the separated set of feature around a datum such as the associated detected complex, and further aids in comparing the separated set of features with a set of predetermined templates.
Next the process includes comparing the aligned set of features to a set of predetermined templates to classify the associated complex. In some embodiments, the predetermined templates are a set of identified complexes associated with known cardiac arrhythmias that would assist in comparing and classifying the extracted set of features. In some embodiments, a therapy is provided to the heart based on the outcome of the classification. The above described process repeats itself on a continuous basis for a real-time classification of complexes from the sensed cardiac signal.